Application of the Adaptive Shrinkage Genetic Algorithm in the Feasible Region to TEM Conductive Thin Layer Inversion
Li Xiu, Xue Guoqiang, Song Jianping,Guo Wenbo, Wu Junjie, and Shen Meifang School of Electronic & Information Engineering, Xi An Jiaotong University, Xi An, 710049. School of Geology & Survey Engineering, Chang An University, Xi An, 710054,
Combining the adaptive shrinkage genetic algorithm in the feasible region with the imaging of apparent vertical conductance differential, we have inverted the TEM conductive thin layer. The result of the inversion demonstrates that by adaptive shrinkage in the feasible region, the calculation speed accelerates and the calculation precision improves. To a certain extent, in this method we surmount the transient electromagnetic sounding equivalence and reduced equivalence scope. Comparison of the inverted result with the forward curve clearly shows that we can image the conductive thin layer.